Plotting Maps

United States Map

ref- https://plot.ly/python/choropleth-maps/#choropleth-map-with-plotlyexpress

import pandas as pd
import plotly.graph_objects as go

/Users/chandrasekharbotcha/opt/anaconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning:

numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject

/Users/chandrasekharbotcha/opt/anaconda3/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning:

numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject

df = pd.read_csv('data/2011_US_AGRI_Exports')

df.head()

	code	state	category	total exports	beef	pork	poultry	dairy	fruits fresh	fruits proc	total fruits	veggies fresh	veggies proc	total veggies	corn	wheat	cotton	text
0	AL	Alabama	state	1390.63	34.4	10.6	481.0	4.06	8.0	17.1	25.11	5.5	8.9	14.33	34.9	70.0	317.61	Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...
1	AK	Alaska	state	13.31	0.2	0.1	0.0	0.19	0.0	0.0	0.00	0.6	1.0	1.56	0.0	0.0	0.00	Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...
2	AZ	Arizona	state	1463.17	71.3	17.9	0.0	105.48	19.3	41.0	60.27	147.5	239.4	386.91	7.3	48.7	423.95	Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...
3	AR	Arkansas	state	3586.02	53.2	29.4	562.9	3.53	2.2	4.7	6.88	4.4	7.1	11.45	69.5	114.5	665.44	Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...
4	CA	California	state	16472.88	228.7	11.1	225.4	929.95	2791.8	5944.6	8736.40	803.2	1303.5	2106.79	34.6	249.3	1064.95	California<br>Beef 228.7 Dairy 929.95<br>Frui...

fig = go.Figure(data=go.Choropleth(
        locations = df['code'], # spatial co-ordinates
        z = df['total exports'].astype(float), # Data to be color-coded
        locationmode = 'USA-states', # set of location match entries in 'locations'
        colorscale= 'reds',
        colorbar_title = "Millions USD"
            ))

fig.update_layout(
    title_text = '2011 US Agriculture Exports by State',
    geo_scope = 'usa')
fig.show()

fig = go.Figure(data=go.Choropleth(
        locations=['TX','CA','NY'], # spatial co-ordinates
        z = [1.0,2.0,3.0], # Data to be color-coded
        locationmode = 'USA-states', # set of location match entries in 'locations'
        colorscale= 'portland',
        text=['t1','t2','t3']
            ))

fig.update_layout(
    title_text = 'Three states study',
    geo_scope = 'usa')
fig.show()

df = pd.read_csv('data/2014_World_GDP')
df.head()

	COUNTRY	GDP (BILLIONS)	CODE
0	Afghanistan	21.71	AFG
1	Albania	13.40	ALB
2	Algeria	227.80	DZA
3	American Samoa	0.75	ASM
4	Andorra	4.80	AND

fig = go.Figure(data=go.Choropleth(
        locations = df['CODE'], # spatial co-ordinates
        z = df['GDP (BILLIONS)'], # Data to be color-coded
        
        colorscale= 'reds',
        colorbar_title = "GDP BILLIONS US$"
            ))


fig.update_layout(
    title_text = 'World GDP study',
    geo= dict(
        showframe=True,
        showcoastlines=False,
        projection_type='equirectangular'
        
    
    ))
fig.show()